The proposed work explores the use of energy landscape ideas to study the protein folding problem. The specific aims are (1) to quantify the energy landscape of proteins especially to characterize intermediate degrees of order in partially folded proteins (2) to develop new descriptions of the kinetics of protein folding, so as to assist in the planning and interpretation of new fast kinetics experiments for folding, and (3) to develop new algorithms for the prediction of protein structure using sequence information and to apply these algorithms to important targets, in particular, the hormone binding regions of the steroid hormone receptors. Energy landscape ideas are mathematical techniques for characterizing in statistical terms the energies of the ensemble of configurations of a protein and to study how a protein is guided to its functioning folded states. In addition to analytical approaches, new computer simulation techniques are proposed for predicting structure and characterizing the dominant flow toward the native state. The study of partially folded proteins and the route to folded structures is of direct health interest because misfolded proteins are involved in several human diseases, including Alzheimer's disease. Improved algorithms for structure prediction help in speeding up structure based drug design. Our focus on the hormone receptors is motivated by their importance in breast and prostate cancer, thyroid disease and in disorders of development.